The treatment of canine mast cell tumours with electrochemotherapy with or without surgical excision
Bibliographic record
Abstract
To describe the results of electrochemotherapy (ECT) in dogs with mast cell tumours (MCTs) either as first line therapy or as an adjuvant to surgery. The treatment combines administration of low dose chemotherapeutic drugs with the application of microsecond electric pulses, which cause the temporary permeabilization and increased porosity of the tumour cell membranes. The design of this study is a retrospective case series. A total of 51 dogs with MCTs were included and classified according to ECT procedure into 4 groups (ECT only, 15 cases, intra-surgery ECT, 11, ECT Adjuvant to surgery, 14, Surgery followed by ECT, 11). The four groups (staged with location, size and grade) were evaluated to assess complete or partial remission, disease free interval, overall survival time and local toxicity. In this case series, Boxers, mixed breed and Labrador Retrievers, male dogs, between 4 and 9 years old were more represented. MCTs were predominantly grade 2 (Patnaik) and T stage 0-1, I-1 (World Health Organization). Treated lesions were most commonly identified on the hindlimb and head where curative surgery would involve cosmetic or functional compromise. The intra-surgery group of dogs showed the best disease free interval with Kaplan-Meyer analysis. Local toxicity induced by ECT ranged mostly from 1 to 4 in a 5-point arbitrary scale with 0 - no toxicity to 5 - highest toxicity. In this study, ECT can be applied successfully as an exclusive therapy in smaller MCTs as an alternative to surgery. ECT can be combined with surgery either intra-operatively or post operatively for larger lesions without significant toxicity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".